Intelligent classification of cervical pre-cancerous cells based on the FTIR spectra

Abstract Inspired from the great potential of the Fourier-transform infrared (FTIR) spectroscopy as a screening tool for cervical cancer, this paper proposes an intelligent classification of cervical pre-cancerous cells based on the FTIR spectra. It consists of two parts: the extraction of FTIR characteristics and the intelligent classification of the pre-cancerous cells. Peak-corrected area-based features’ extraction (PCABFE) is introduced as a tool for the first part, while the Hybrid Multilayered Perceptron (HMLP) network is employed to classify the cervical pre-cancerous cells according to the Bethesda classification. Correlation test proves the capability of the proposed PCABFE to be as effective as the manual extraction by human experts, while the HMLP network produces a good classification performance with 97.4% of accuracy.

[1]  M. Diem,et al.  Infrared spectroscopy of human tissue. IV. Detection of dysplastic and neoplastic changes of human cervical tissue via infrared microscopy. , 1998, Cellular and molecular biology.

[2]  B. Rigas,et al.  Infrared spectroscopic study of cervical smears in patients with HIV: implications for cervical carcinogenesis. , 2000, The Journal of laboratory and clinical medicine.

[3]  Robert Wagner,et al.  Technical data mining with evolutionary radial basis function classifiers , 2009, Appl. Soft Comput..

[4]  Nor Ashidi Mat Isa,et al.  An automated cervical pre-cancerous diagnostic system , 2008, Artif. Intell. Medicine.

[5]  N. Dusitsin,et al.  A new approach for the detection of cervical cancer in Thai women. , 2003, Gynecologic oncology.

[6]  B. Rigas,et al.  Cytologically normal cells from neoplastic cervical samples display extensive structural abnormalities on IR spectroscopy: implications for tumor biology. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[7]  S. Argov,et al.  Possible common biomarkers from FTIR microspectroscopy of cervical cancer and melanoma , 2004, Journal of microscopy.

[8]  Rohana Adnan,et al.  Comparative study between Pap smear cytology and FTIR spectroscopy: a new tool for screening for cervical cancer , 2008, Pathology.

[9]  Francis L Martin,et al.  IR microspectroscopy: potential applications in cervical cancer screening. , 2007, Cancer letters.

[10]  Michel Manfait,et al.  Discriminating nevus and melanoma on paraffin-embedded skin biopsies using FTIR microspectroscopy. , 2005, Biochimica et biophysica acta.

[11]  Richard L Lozano,et al.  Comparison of computer-assisted and manual screening of cervical cytology. , 2007, Gynecologic oncology.

[12]  Jing Wang,et al.  FT-IR spectroscopic analysis of normal and cancerous tissues of esophagus. , 2003, World journal of gastroenterology.

[13]  L. J. Cao,et al.  Feature extraction in support vector machine: a comparison of PCA, XPCA and ICA , 2002, Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02..

[14]  B. Rigas,et al.  Infrared spectroscopy of normal and abnormal cervical smears: evaluation by principal component analysis. , 1997, Gynecologic oncology.

[15]  B. Rigas,et al.  Infrared spectroscopy of exfoliated human cervical cells: evidence of extensive structural changes during carcinogenesis. , 1991, Proceedings of the National Academy of Sciences of the United States of America.

[16]  M. Diem,et al.  Fourier transform infrared (FTIR) spectral mapping of the cervical transformation zone, and dysplastic squamous epithelium. , 2004, Gynecologic oncology.

[17]  W. Faught,et al.  Comparison of Fourier-transform infrared spectroscopic screening of exfoliated cervical cells with standard Papanicolaou screening. , 1997, Gynecologic oncology.

[18]  Jenq-Neng Hwang,et al.  Integration of neural networks and decision tree classifiers for automated cytology screening , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.

[19]  Kagan Tumer,et al.  Ensembles of radial basis function networks for spectroscopic detection of cervical precancer , 1998, IEEE Transactions on Biomedical Engineering.

[20]  M. Gani,et al.  Classification of Cervical Cancer Cells using FTIR Data , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[21]  D. V. Coleman.  Evaluation of automated systems for the primary screening of cervical smears , 1998 .

[22]  R. Ashfaq,et al.  Detection of endocervical component by PAPNET™ system on negative cervical smears , 1996, Diagnostic cytopathology.

[23]  George D. C. Cavalcanti,et al.  PCA feature extraction for protein structure prediction , 2003, Proceedings of the International Joint Conference on Neural Networks, 2003..

[24]  Gabriele Schackert,et al.  Detection and grading of human gliomas by FTIR spectroscopy and a genetic classification algorithm , 2002, SPIE BiOS.

[25]  M. Y. Mashor.  Hybrid multilayered perceptron networks , 2000, Int. J. Syst. Sci..

[26]  Li Zhang,et al.  In vivo and in situ detection of colorectal cancer using Fourier transform infrared spectroscopy. , 2005, World journal of gastroenterology.

[27]  Nor Ashidi Mat Isa,et al.  Clustered-Hybrid Multilayer Perceptron network for pattern recognition application , 2011, Appl. Soft Comput..

[28]  Zhong Li,et al.  Automated classification of Pap smear tests using neural networks , 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

[29]  E. Domingo,et al.  Epidemiology and prevention of cervical cancer in Indonesia, Malaysia, the Philippines, Thailand and Vietnam. , 2008, Vaccine.

[30]  Christoph Krafft,et al.  Delimitation of squamous cell cervical carcinoma using infrared microspectroscopic imaging , 2006, Analytical and bioanalytical chemistry.

[31]  Ranjit Sahu,et al.  Probing cell proliferation in the human colon using vibrational spectroscopy: a novel use of FTIR-microspectroscopy , 2004 .

[32]  Yaw-Bin Huang,et al.  Characterization of human cervical precancerous tissue through the fourier transform infrared microscopy with mapping method. , 2003, Gynecologic oncology.

[33]  H S Cronjé,et al.  Screening for cervical cancer in the developing world. , 2005, Best practice & research. Clinical obstetrics & gynaecology.

[34]  K. Yano,et al.  Direct measurement of human lung cancerous and noncancerous tissues by fourier transform infrared microscopy: can an infrared microscope be used as a clinical tool? , 2000, Analytical biochemistry.

[35]  J Prat,et al.  Rescreening of atypical cervicovaginal smears using PAPNET , 1998, Cancer.

[36]  Melissa J Romeo,et al.  Removal of blood components from cervical smears: implications for cancer diagnosis using FTIR spectroscopy. , 2003, Biopolymers.

[37]  Nor Ashidi Mat Isa,et al.  Modified Recursive Least Squares algorithm to train the Hybrid Multilayered Perceptron (HMLP) network , 2010, Appl. Soft Comput..

[38]  J Chilcott,et al.  Liquid-based cytology in cervical screening: an updated rapid and systematic review and economic analysis. , 2004, Health technology assessment.

[39]  Sankar K. Pal,et al.  Staging of cervical cancer with soft computing , 2000, IEEE Transactions on Biomedical Engineering.

[40]  P. Lasch,et al.  IR spectra and IR spectral maps of individual normal and cancerous cells. , 2002, Biopolymers.

[41]  Max Diem,et al.  Imaging of colorectal adenocarcinoma using FT-IR microspectroscopy and cluster analysis. , 2004, Biochimica et biophysica acta.

[42]  S. Sundaresan,et al.  Fourier-transform infrared spectroscopic study of characteristic molecular structure in cancer cells of esophagus: an exploratory study. , 2007, Cancer detection and prevention.

[43]  Christoph Krafft,et al.  Classification of malignant gliomas by infrared spectroscopy and linear discriminant analysis. , 2006, Biopolymers.

[44]  Cullen Schaffer,et al.  Selecting a classification method by cross-validation , 1993, Machine Learning.